############################################################################
# Create population, provide noisy input to each cell, connect network
############################################################################
# create network object instance
network = Network(dt=PSET.dt, tstop=PSET.tstop, v_init=PSET.v_init,
celsius=PSET.celsius,
OUTPUTPATH=PSET.OUTPUTPATH)
# create populations iteratively
for name, pop_args, rotation_args, POP_SIZE in zip(
PSET.populationParameters['me_type'],
PSET.populationParameters['pop_args'],
PSET.populationParameters['rotation_args'],
(PSET.populationParameters['POP_SIZE']).astype(int)):
network.create_population(CWD=PSET.CWD, CELLPATH=PSET.CELLPATH,
Cell=NetworkCell, POP_SIZE=POP_SIZE,
name=name, cell_args=PSET.cellParameters[name],
pop_args=pop_args, rotation_args=rotation_args)
# tic-toc
if RANK == 0:
create_population_time = time() - tic
print('Populations initialized in {} seconds'.format(create_population_time))
logfile.write('population {}\n'.format(create_population_time))
tic = time()
# Sync MPI threads as populations may take a different amount of
# time across RANKs. All neurons must have been created before connections
# are made
COMM.Barrier()
if __name__ == '__main__':
############################################################################
# Main simulation
############################################################################
# create directory for output:
if not os.path.isdir(OUTPUTPATH):
if RANK == 0:
os.mkdir(OUTPUTPATH)
COMM.Barrier()
# instantiate Network:
network = Network(**networkParameters)
# create E and I populations:
for name, size in zip(population_names, population_sizes):
network.create_population(name=name, POP_SIZE=size,
**populationParameters)
# create excitatory background synaptic activity for each cell
# with Poisson statistics
for cell in network.populations[name].cells:
idx = cell.get_rand_idx_area_norm(section='allsec', nidx=64)
for i in idx:
syn = Synapse(cell=cell, idx=i, syntype='Exp2Syn',
weight=0.002,
**dict(tau1=0.2, tau2=1.8, e=0.))
syn.set_spike_times_w_netstim(interval=100.,
seed=np.random.rand() * 2**32 - 1
)
# create network object instance
network = Network(dt=PSET.dt,
tstop=PSET.tstop,
v_init=PSET.v_init,
celsius=PSET.celsius,
OUTPUTPATH=PSET.OUTPUTPATH)
# create populations iteratively
for name, pop_args, rotation_args, POP_SIZE in zip(
PSET.populationParameters['me_type'],
PSET.populationParameters['pop_args'],
PSET.populationParameters['rotation_args'],
(PSET.populationParameters['POP_SIZE']).astype(int)):
network.create_population(CWD=PSET.CWD,
CELLPATH=PSET.CELLPATH,
Cell=NetworkCell,
POP_SIZE=POP_SIZE,
name=name,
cell_args=PSET.cellParameters[name],
pop_args=pop_args,
rotation_args=rotation_args)
# tic-toc
if RANK == 0:
create_population_time = time() - tic
print('Populations initialized in {} seconds'.format(
create_population_time))
logfile.write('population {}\n'.format(create_population_time))
tic = time()
# Sync MPI threads as populations may take a different amount of
# time across RANKs. All neurons must have been created before connections
# are made
if __name__ == '__main__':
############################################################################
# Main simulation
############################################################################
# create directory for output:
if not os.path.isdir(OUTPUTPATH):
if RANK == 0:
os.mkdir(OUTPUTPATH)
COMM.Barrier()
# instantiate Network:
network = Network(**networkParameters)
# create E and I populations:
for name, size in zip(population_names, population_sizes):
network.create_population(name=name, POP_SIZE=size,
**populationParameters)
# create excitatpry background synaptic activity for each cell
# with Poisson statistics
for cell in network.populations[name].cells:
idx = cell.get_rand_idx_area_norm(section='allsec', nidx=64)
for i in idx:
syn = Synapse(cell=cell, idx=i, syntype='Exp2Syn',
weight=0.002,
**dict(tau1=0.2, tau2=1.8, e=0.))
syn.set_spike_times_w_netstim(interval=200.)
# create connectivity matrices and connect populations:
for i, pre in enumerate(population_names):